A multi-agent hierarchical Bayesian model corrects selection bias in LLM user feedback via topic clustering and reweighting with mild priors on the feedback channel to recover accurate aggregate quality estimates.
The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo
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With 100 anchors the Bayesian linear corrector matches or beats the Neural-ODE flow on distribution recovery while both fix mean offset; with 1500 anchors the flow wins on MAE, Pearson correlation, and KL divergence.
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Correcting Selection Bias in Sparse User Feedback for Large Language Model Quality Estimation: A Multi-Agent Hierarchical Bayesian Approach
A multi-agent hierarchical Bayesian model corrects selection bias in LLM user feedback via topic clustering and reweighting with mild priors on the feedback channel to recover accurate aggregate quality estimates.
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Two Ways to De-Bias an LLM-as-a-Judge: A Continuous-Score Comparison of Hierarchical Bayesian Calibration and Neural-ODE Score Transport
With 100 anchors the Bayesian linear corrector matches or beats the Neural-ODE flow on distribution recovery while both fix mean offset; with 1500 anchors the flow wins on MAE, Pearson correlation, and KL divergence.